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VisualRank - Applying PageRank to Large-Scale Image Search

VisualRank - Applying PageRank to Large-Scale Image Search. Presenter ： Chien-Hsing Chen Author: Yushi Jing Shumeet Baluja. 2008.PAMI (IEEE Transactions on Pattern Analysis and Machine Intelligence). Outline. Motivation Objective Method Experiments Conclusion Comment.

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VisualRank - Applying PageRank to Large-Scale Image Search

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  1. VisualRank- Applying PageRank to Large-Scale Image Search Presenter：Chien-Hsing Chen Author: Yushi Jing ShumeetBaluja 2008.PAMI (IEEE Transactions on Pattern Analysis and Machine Intelligence)

  2. Outline • Motivation • Objective • Method • Experiments • Conclusion • Comment

  3. Motivation • retrieved images may be not fitting (satisfactory) or not diverse How the image could be retrieved ? The news shows a disappointed salesman of Coca Colareturns from his Middle East assignment. A friend asked, “Why weren’t you successful with the Arabs?”

  4. Example 2

  5. Objective • improve quality of image retrieval by rearrange the results of Google search engine • incorrect retrieval • d80 Coca Cola • diversity (retrieved images should be different) • You should know: • 1. adjacency matrix, matrix product • 2. eigenvector, PageRank()

  6. Main idea 1/2 0.6 • Rearrange • previous works • Query-to-images • in this paper • Images-to-images 0.9 0.4 0.3 0.2 0.1 0.1

  7. Main idea 2/2 • Rearrange • Images-to-images • 1. • similar local features • Web site source • my homepage V.S. Yahoo • 2. • diversity 0.6 0.9 0.4 0.3 0.1 0.2 0.1

  8. Images-to-images 1/2 x4 x5 x1 x2 x3 x1 x2 x3 Top ranked images : x4 x5 How to connect between vertexes ? (how to build edge sets) Adjacency matrix

  9. Images-to-images 2/2 x4 x5 x1 x2 x3 x1 x2 x3 x4 Top ranked images : x5 How to give the scores between vertexes ? Adjacency matrix

  10. How to connect between vertexes ? Common local features 1/2 • which pair has most number of common (similar) local features? (a) local features, such as hands, eyes, are similar (g) local features are very different

  11. Common local features 2/2

  12. [n × n] matrix eigenvector image relationship • Which image has most number of common (similar) local features? • A image of which features are similar to the features in the other images. The image is important. × ＝ The entry is evaluated by “local features” uniform ?

  13. PageRank PageRank() concerns the properties of “Hub” and “Authority” Web sites appearing in front of the Google responds are more important than that appearing in back of the ones. d 8 9 11 3 100

  14. image diversity Top ranked images with respect to diversity:

  15. Experiments

  16. c

  17. c

  18. Conclusion • arrange the images from the results of Google search engine

  19. Comment • Advantage • The aspect is novel and easy to implement. • Drawback • less discussion in diversity • Application • responds of search engine • an option is to cluster the resulted images

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